
A key characteristic of our proposed Spacing Loss is that the latent space regularization that it offers can effectively act as an add-on to existing methodologies. We showcase this capability while evaluating in single-stage setting. In Tab. 1, we organise different dataset splits based on the balance between the number of classes in labeled and unlabeled pool. The concise notation in Row 2 can be expanded as: dataset\(-\) total_class_count\(-\) labeled_classes\(-\) 
unlabeled_classes. The latent space separation induced by Spacing Loss helps to improve the class discovery capability on all settings. It is interesting to note that the improvement is more pronounced in the more pragmatic setting, where the split of classes between the labeled and unlabeled pool is skewed. t-SNE [1]} visualization of backbone features in fig:tsne shows good separation in these latent representations of novel categories in CIFAR-10-5-5 setting.
